Amazon Redshift vs Census

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

Census

Stacks22

Followers27

Votes0

Amazon Redshift vs Census: What are the differences?

Introduction:

Amazon Redshift and Census are both data warehousing solutions used for storing and analyzing large volumes of data. However, there are key differences between the two that cater to different needs and requirements.

1. Scalability: Amazon Redshift is highly scalable and can easily handle petabytes of data, making it suitable for enterprise-level data analysis. On the other hand, Census is more focused on providing a simple and intuitive interface for smaller data sets, making it ideal for small to mid-sized businesses that do not require massive scalability.

2. Pricing Model: Amazon Redshift follows a pay-as-you-go pricing model, where users are charged based on the amount of data stored and processed. In contrast, Census offers a flat-rate pricing model, making it easier for users to budget and plan their expenses without unexpected costs.

3. Integration with External Tools: Amazon Redshift has strong integration capabilities with various external tools and services, allowing for seamless data transfer and analysis across different platforms. Census, on the other hand, is more limited in terms of integration options, which may be a consideration for companies with complex data pipelines involving multiple tools.

4. Data Security: Amazon Redshift offers comprehensive security features such as data encryption, access controls, and compliance certifications to ensure data protection and regulatory compliance. While Census also offers similar security measures, it may lack some of the advanced security features provided by Amazon Redshift, which could be a deciding factor for organizations with strict security requirements.

5. Query Performance: Amazon Redshift is known for its high query performance and can efficiently handle complex analytical queries on large volumes of data. Census, while efficient for smaller data sets, may not perform as well as Amazon Redshift when it comes to processing complex queries at scale, which could impact the user experience and overall data analysis capabilities.

6. Customization Options: Amazon Redshift allows for extensive customization and optimization of the data warehouse environment, enabling users to fine-tune performance and scalability based on their specific requirements. Census, on the other hand, may offer fewer customization options, which could limit the flexibility and adaptability of the data warehouse to unique business needs and workflows.

In Summary, Amazon Redshift and Census differ in scalability, pricing model, integration capabilities, data security, query performance, and customization options catering to diverse business requirements and data analysis needs.

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Advice on Amazon Redshift, Census

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Detailed Comparison

Amazon Redshift	Census
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.	It syncs your data warehouse with CRM & go-to-market tools. Get your customer success, sales & marketing teams on the same page by sharing the same customer data.
Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	Turn your warehouse into a Customer Data Platform; Sync with customer facing tools; No more data outages
Statistics
Stacks 1.5K	Stacks 22
Followers 1.4K	Followers 27
Votes 108	Votes 0
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	No community feedback yet
Integrations
SQLite MySQL Oracle PL/SQL	Google BigQuery Outreach.io Google Sheets Pipedrive Snowflake Customer.io Iterable Marketo Braze

What are some alternatives to Amazon Redshift, Census?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.